






'''simple docstring'''







import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


__lowerCAmelCase        =							logging.get_logger(__name__)

enable_full_determinism()




class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Optional[Any]      			=     UNetaDModel
  _a  : str      			=     '''sample'''







  @property
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			4
         a_						     =			3
         a_						     =			(32, 32)

         a_						     =			floats_tensor((batch_size, num_channels) + sizes	).to(UpperCamelCase__	)
         a_						     =			torch.tensor([10]	).to(UpperCamelCase__	)

         return {"sample": noise, "timestep": time_step}







  @property
  def    _a       (			self	):






         """simple docstring"""


         return (3, 32, 32)







  @property
  def    _a       (			self	):






         """simple docstring"""


         return (3, 32, 32)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			{
             'block_out_channels': (32, 64),
             'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
             'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
             'attention_head_dim': 3,
             'out_channels': 3,
             'in_channels': 3,
             'layers_per_block': 2,
             'sample_size': 32,
         }
         a_						     =			self.dummy_input
         return init_dict, inputs_dict





class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Any      			=     UNetaDModel
  _a  : Dict      			=     '''sample'''







  @property
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			4
         a_						     =			4
         a_						     =			(32, 32)

         a_						     =			floats_tensor((batch_size, num_channels) + sizes	).to(UpperCamelCase__	)
         a_						     =			torch.tensor([10]	).to(UpperCamelCase__	)

         return {"sample": noise, "timestep": time_step}







  @property
  def    _a       (			self	):






         """simple docstring"""


         return (4, 32, 32)







  @property
  def    _a       (			self	):






         """simple docstring"""


         return (4, 32, 32)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			{
             'sample_size': 32,
             'in_channels': 4,
             'out_channels': 4,
             'layers_per_block': 2,
             'block_out_channels': (32, 64),
             'attention_head_dim': 32,
             'down_block_types': ('DownBlock2D', 'DownBlock2D'),
             'up_block_types': ('UpBlock2D', 'UpBlock2D'),
         }
         a_						     =			self.dummy_input
         return init_dict, inputs_dict







  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'   ,	output_loading_info=UpperCamelCase__	)

         self.assertIsNotNone(UpperCamelCase__	)
         self.assertEqual(len(loading_info['missing_keys']	)   ,	0	)

         model.to(UpperCamelCase__	)
         a_						     =			model(**self.dummy_input	).sample

         assert image is not None, "Make sure output is not None"







  @unittest.skipIf(torch_device != 'cuda'   ,	'This test is supposed to run on GPU'	)
  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'   ,	output_loading_info=UpperCamelCase__	)
         model.to(UpperCamelCase__	)
         a_						     =			model(**self.dummy_input	).sample

         assert image is not None, "Make sure output is not None"







  @unittest.skipIf(torch_device != 'cuda'   ,	'This test is supposed to run on GPU'	)
  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'   ,	output_loading_info=UpperCamelCase__	)
         model_accelerate.to(UpperCamelCase__	)
         model_accelerate.eval()

         a_						     =			torch.randn(
             1   ,	model_accelerate.config.in_channels   ,	model_accelerate.config.sample_size   ,	model_accelerate.config.sample_size   ,	generator=torch.manual_seed(0	)   ,	)
         a_						     =			noise.to(UpperCamelCase__	)
         a_						     =			torch.tensor([10] * noise.shape[0]	).to(UpperCamelCase__	)

         a_						     =			model_accelerate(UpperCamelCase__   ,	UpperCamelCase__	)['sample']

         # two models don't need to stay in the device at the same time
         del model_accelerate
         torch.cuda.empty_cache()
         gc.collect()

         a_     ,	a_						     =			UNetaDModel.from_pretrained(
             'fusing/unet-ldm-dummy-update'   ,	output_loading_info=UpperCamelCase__   ,	low_cpu_mem_usage=UpperCamelCase__	)
         model_normal_load.to(UpperCamelCase__	)
         model_normal_load.eval()
         a_						     =			model_normal_load(UpperCamelCase__   ,	UpperCamelCase__	)['sample']

         assert torch_all_close(UpperCamelCase__   ,	UpperCamelCase__   ,	rtol=1e-3	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'	)
         model.eval()
         model.to(UpperCamelCase__	)

         a_						     =			torch.randn(
             1   ,	model.config.in_channels   ,	model.config.sample_size   ,	model.config.sample_size   ,	generator=torch.manual_seed(0	)   ,	)
         a_						     =			noise.to(UpperCamelCase__	)
         a_						     =			torch.tensor([10] * noise.shape[0]	).to(UpperCamelCase__	)

         with torch.no_grad():
                a_						     =			model(UpperCamelCase__   ,	UpperCamelCase__	).sample

         a_						     =			output[0, -1, -3:, -3:].flatten().cpu()
         # fmt: off
         a_						     =			torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]	)
         # fmt: on

         self.assertTrue(torch_all_close(UpperCamelCase__   ,	UpperCamelCase__   ,	rtol=1e-3	)	)





class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Dict      			=     UNetaDModel
  _a  : str      			=     '''sample'''







  @property
  def    _a       (			self   ,	UpperCamelCase__=(32, 32)	):






         """simple docstring"""


         a_						     =			4
         a_						     =			3

         a_						     =			floats_tensor((batch_size, num_channels) + sizes	).to(UpperCamelCase__	)
         a_						     =			torch.tensor(batch_size * [10]	).to(dtype=torch.intaa   ,	device=UpperCamelCase__	)

         return {"sample": noise, "timestep": time_step}







  @property
  def    _a       (			self	):






         """simple docstring"""


         return (3, 32, 32)







  @property
  def    _a       (			self	):






         """simple docstring"""


         return (3, 32, 32)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			{
             'block_out_channels': [32, 64, 64, 64],
             'in_channels': 3,
             'layers_per_block': 1,
             'out_channels': 3,
             'time_embedding_type': 'fourier',
             'norm_eps': 1e-6,
             'mid_block_scale_factor': math.sqrt(2.0	),
             'norm_num_groups': None,
             'down_block_types': [
                 'SkipDownBlock2D',
                 'AttnSkipDownBlock2D',
                 'SkipDownBlock2D',
                 'SkipDownBlock2D',
             ],
             'up_block_types': [
                 'SkipUpBlock2D',
                 'SkipUpBlock2D',
                 'AttnSkipUpBlock2D',
                 'SkipUpBlock2D',
             ],
         }
         a_						     =			self.dummy_input
         return init_dict, inputs_dict







  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'   ,	output_loading_info=UpperCamelCase__	)
         self.assertIsNotNone(UpperCamelCase__	)
         self.assertEqual(len(loading_info['missing_keys']	)   ,	0	)

         model.to(UpperCamelCase__	)
         a_						     =			self.dummy_input
         a_						     =			floats_tensor((4, 3) + (256, 256)	).to(UpperCamelCase__	)
         a_						     =			noise
         a_						     =			model(**UpperCamelCase__	)

         assert image is not None, "Make sure output is not None"







  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'	)
         model.to(UpperCamelCase__	)

         a_						     =			4
         a_						     =			3
         a_						     =			(256, 256)

         a_						     =			torch.ones((batch_size, num_channels) + sizes	).to(UpperCamelCase__	)
         a_						     =			torch.tensor(batch_size * [1e-4]	).to(UpperCamelCase__	)

         with torch.no_grad():
                a_						     =			model(UpperCamelCase__   ,	UpperCamelCase__	).sample

         a_						     =			output[0, -3:, -3:, -1].flatten().cpu()
         # fmt: off
         a_						     =			torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]	)
         # fmt: on

         self.assertTrue(torch_all_close(UpperCamelCase__   ,	UpperCamelCase__   ,	rtol=1e-2	)	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'	)
         model.to(UpperCamelCase__	)

         a_						     =			4
         a_						     =			3
         a_						     =			(32, 32)

         a_						     =			torch.ones((batch_size, num_channels) + sizes	).to(UpperCamelCase__	)
         a_						     =			torch.tensor(batch_size * [1e-4]	).to(UpperCamelCase__	)

         with torch.no_grad():
                a_						     =			model(UpperCamelCase__   ,	UpperCamelCase__	).sample

         a_						     =			output[0, -3:, -3:, -1].flatten().cpu()
         # fmt: off
         a_						     =			torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]	)
         # fmt: on

         self.assertTrue(torch_all_close(UpperCamelCase__   ,	UpperCamelCase__   ,	rtol=1e-2	)	)







  def    _a       (			self	):






         """simple docstring"""


         pass












'''simple docstring'''







def       __UpperCamelCase					(      lowercase_       :  int = 1_000_000   ):
       """simple docstring"""




       a_						     =			set(range(3   ,       lowercase_   ,       2   )   )
       primes.add(2   )
       for p in range(3   ,       lowercase_   ,       2   ):
              if p not in primes:
                     continue
              primes.difference_update(set(range(p * p   ,       lowercase_   ,       lowercase_   )   )   )

       a_						     =			[float(lowercase_   ) for n in range(limit + 1   )]

       for p in primes:
              for n in range(lowercase_   ,       limit + 1   ,       lowercase_   ):
                     phi[n] *= 1 - 1 / p

       return int(sum(phi[2:]   )   )


if __name__ == "__main__":
  print(f"""{solution() = }""")




